diff --git a/3. RNN/rnn.ipynb b/3. RNN/rnn.ipynb new file mode 100644 index 0000000..3887340 --- /dev/null +++ b/3. RNN/rnn.ipynb @@ -0,0 +1,8703 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "# !unzip -q /content/en-ner-conll-2003.zip -d /content/" + ], + "metadata": { + "id": "am0MK_BNa4eB" + }, + "id": "am0MK_BNa4eB", + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "36c02fac", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "36c02fac", + "outputId": "992e4fef-67b1-490e-adbb-22fba93a0dc4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchtext/vocab/__init__.py:4: UserWarning: \n", + "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", + "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", + " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n", + "/usr/local/lib/python3.10/dist-packages/torchtext/utils.py:4: UserWarning: \n", + "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", + "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", + " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import gensim\n", + "from gensim.models import KeyedVectors\n", + "import numpy as np\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "import matplotlib.pyplot as plt\n", + "from keras.regularizers import l2\n", + "from collections import Counter\n", + "from torchtext.vocab import vocab\n", + "from tqdm.notebook import tqdm\n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "id": "db84429c", + "metadata": { + "id": "db84429c" + }, + "source": [ + "### Declare path" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc2539ae", + "metadata": { + "id": "fc2539ae" + }, + "outputs": [], + "source": [ + "data_dir_path = 'en-ner-conll-2003'\n", + "train_path = os.path.join(data_dir_path, 'train', 'train.tsv')\n", + "dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')\n", + "dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')\n", + "dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')\n", + "test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')\n", + "test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')\n", + "word2vec_file_path = 'word2vec_100_3_polish.bin'" + ] + }, + { + "cell_type": "markdown", + "id": "e4ea0458", + "metadata": { + "id": "e4ea0458" + }, + "source": [ + "### Load files" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4e038df7", + "metadata": { + "id": "4e038df7" + }, + "outputs": [], + "source": [ + "train_data = pd.read_csv(train_path, sep='\\t', usecols=[0, 1], header=None, names=['label', 'text'])\n", + "dev_texts_data = pd.read_csv(dev_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])\n", + "dev_labels_data = pd.read_csv(dev_labels_path, sep='\\t', usecols=[0], header=None, names=['label'])\n", + "test_texts_data = pd.read_csv(test_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])" + ] + }, + { + "cell_type": "markdown", + "id": "f2bc73a6", + "metadata": { + "id": "f2bc73a6" + }, + "source": [ + "### Build Vocab" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e0022f7b", + "metadata": { + "id": "e0022f7b" + }, + "outputs": [], + "source": [ + "def build_vocab(dataset):\n", + " counter = Counter()\n", + " for document in dataset:\n", + " counter.update(document)\n", + " return vocab(counter, specials=[\"\", \"\", \"\", \"\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7d9fb428", + "metadata": { + "id": "7d9fb428" + }, + "outputs": [], + "source": [ + "train_X = train_data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n", + "v = build_vocab(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7e793633", + "metadata": { + "id": "7e793633" + }, + "outputs": [], + "source": [ + "itos = v.get_itos()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d3cca05a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d3cca05a", + "outputId": "053dc5b9-23f8-4f63-9352-68d1bd318fa6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['', '', '', '', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', 'peter', 'blackburn', 'brussels', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', 'we', 'do', 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', 'bse', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', 'meeting', 'of', 'causing', 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'skying', 'flighted', 'pavilion', 'inswinging', 'saqlain', 'edgbaston', 'lombardi', 'polti', 'clerq', 'jemison', 'servais', 'knaven', 'tunbridge', 'hove', 'athey', 'saggers', 'watkin', 'solanki', 'barnett', 'pooley', 'ramprakash', 'fay', 'mcgeechan', 'grobbelaar', 'bruce', 'zifa', 'pamire', 'duvillard', 'argyle', 'segers', 'fashanu', 'heng', 'suan', 'lim', 'pleaded', 'accepting', 'jenson', 'krizan', 'lila', 'osterloh', 'nanne', 'dahlman', 'slide', 'yankee', 'mulholland', 'kingdome', 'ejected', 'marzano', 'brushed', 'wengert', 'herrera', 'minors', 'baldwin', 'ozzie', 'guillen', 'nilsson', 'offerman', 'rick', 'huisman', 'hentgen', 'tossing', 'orel', 'hershiser', 'belle', 'yorkers', 'aggressiveness', 'adjusting', 'louder', 'messed', 'adrenalin', 'lanes', 'pitching', 'winningest', 'ron', 'wright', 'greenville', 'phenom', 'andruw', 'abdellaoui', 'graef', 'honour', 'assembled', 'quartets', 'anchor', 'autograph', 'ornate', 'dillard', 'lindy', 'remigino', 'hines', 'hasely', 'crawford', 'inspired', 'gina', 'tillman', 'bachini', 'emon', 'bois', 'bronckhorst', 'ferdy', 'positively', 'neighbourly', 'consulates', 'thessaloniki', 'observance', 'picks', 'bold', 'ogilvy', 'mather', 'communication', 'linked', 'divorces', 'romantically', 'petition', 'presenter', 'mocked', 'gymnasium', 'liffe', 'apt', 'automated', 'pit', 'tabular', 'gilt', 'eurolira', 'euroswiss', 'installs', 'kinshasa', 'cne', 'deployed', 'deadlines', 'yoka', 'lye', 'mudaba', 'progressively', 'maniema', 'shaba', 'bandundu', 'installation', 'mobutu', 'sese', 'seko', 'object', 'insists', 'ikimi', 'wanting', 'ministerial', 'continuation', 'saro', 'wiwa', 'restricted', 'beatrix', 'guns', 'forbidden', 'categories', 'djamena', 'idriss', 'deby', 'nomads', 'fraternite', 'matin', 'establishes', 'bauza', 'donwahi', 'douati', 'alphonse', 'illicit', 'swine', 'jour', 'lakpe', 'appoints', 'severin', 'kouame', 'tanny', 'mahala', 'zvornik', 'lieutenant', 'marriner', 'retaliation', 'angry', 'mob', 'barreled', 'lightning', 'berovo', 'cathedral', 'thunderstorm', 'kornblum', 'milosevic', 'greste', 'slobodan', 'defuse', 'manipulation', 'srpska', 'bolster', 'patron', 'rectify', 'expulsions', 'blessing', 'banja', 'luka', 'biljana', 'plavsic', 'oversee', 'mini', 'choosing', 'comprised', 'rbls', 'rao', 'roubles', 'ffk', 'illiquid', 'harder', 'artyom', 'danielyan', 'kosovo', 'boycotted', 'institutions', 'bajgora', 'donje', 'ljupce', 'podujevo', 'celopek', 'revoked', 'cracked', 'moderates', 'cradle', 'meningitis', 'emanuil', 'ceausu', 'babes', 'infectious', 'virus', 'lasts', 'gastro', 'intestinal', 'tract', 'vukasin', 'filipovic', 'supplying', 'staging', 'mismanagement', 'finmin', 'raul', 'matos', 'azocar', 'imf', 'hosted', 'seminar', 'metric', 'sail', 'cartagena', 'coffee', 'ocei', 'foods', 'jpr', 'cops', 'racket', 'thirteen', 'lago', 'nacion', 'credibility', 'undermined', 'scandals', 'indictment', 'piotti', 'dishonest', 'purged', 'brave', 'kandir', 'cetes', 'nervousness', 'ordinated', 'acceptances', 'pagares', 'masked', 'oaxaca', 'assaults', 'credits', 'oversupply', 'shortfall', 'inflow', 'lezama', 'newroom', 'chilpancingo', 'bickering', 'acatepec', 'fanned', 'intimidate', 'stirred', 'agir', 'ensemble', 'pour', 'droits', 'exerted', 'psychological', 'grabs', 'aguirre', 'pessina', 'restaurants', 'travelled', 'condemn', 'punish', 'landless', 'peasants', 'countryside', 'ecumenical', 'endemic', 'massacred', 'para', 'lucas', 'moreira', 'neves', 'bishops', 'flees', 'passport', 'consular', 'soliciting', 'icac', 'graft', 'bribe', 'scams', 'honduran', 'passports', 'thinks', 'solve', 'woes', 'rajapat', 'warnings', 'pullouts', 'chatichai', 'choonhavan', 'chavalit', 'yongchaiyudh', 'aspiration', 'insincere', 'slowdown', 'switched', 'monarchy', 'yassin', 'ramle', 'fundamentalist', 'wrecking', 'confined', 'freeing', 'skhirat', 'purely', 'vital', 'conlon', 'triumphal', 'abruptly', 'eavesdrop', 'conversations', 'campaigns', 'invaluable', 'captivated', 'worried', 'senator', 'dianne', 'feinstein', 'bump', 'dogged', 'wrongdoing', 'misconduct', 'questionable', 'judgment', 'selecting', 'advisers', 'revert', 'revelled', 'heartland', 'hoarse', 'voice', 'resolute', 'wit', 'stilted', 'glib', 'piper', 'jaffray', 'compiled', 'gen', 'muscular', 'dystrophy', 'technique', 'telethon', 'method', 'muscle', 'degenerates', 'adulthood', 'dystrophin', 'altered', 'minimise', 'susceptibility', 'injected', 'mice', 'bred', 'genes', 'experiment', 'fibers', 'diminishing', 'oilseeds', 'oilseed', 'assn', 'cargill', 'mitsui', 'dreyfus', 'dns', 'ww', 'donation', 'taichung', 'procurement', 'soybeans', 'bunge', 'pnw', 'pl', 'buyer', 'upland', 'sorghum', 'pima', 'poultry', 'fruits', 'gaining', 'quota', 'onic', 'panel', 'cutout', 'offal', 'moines', 'steer', 'uneven', 'goldstein', 'hiring', 'matched', 'heist', 'jewelry', 'showboat', 'capt', 'andrews', 'videotape', 'suitcase', 'resembling', 'wholesaler', 'schein', 'suitcases', 'closet', 'jewellers', 'twenties', 'distract', 'mcgilley', 'description', 'simpson', 'hints', 'financially', 'jury', 'gag', 'prohibits', 'acquittal', 'crying', 'blues', 'erroneous', 'jam', 'distanced', 'buffalo', 'wildly', 'supportive', 'gifts', 'supermarket', 'reprinted', 'editions', 'vitriol', 'journalism', 'dignify', 'surgeon', 'discarding', 'amputated', 'improperly', 'disposed', 'deformed', 'infant', 'prosthesis', 'orthopedic', 'educational', 'freezer', 'spoiled', 'crab', 'trap', 'flesh', 'cop', 'shrub', 'guys', 'bagged', 'checker', 'pines', 'toting', 'earl', 'feugill', 'camouflaged', 'shaggy', 'freeze', 'marines', 'attaching', 'strips', 'burlap', 'disguise', 'staking', 'robberies', 'stakeout', 'dla', 'logistics', 'nbc', 'consultant', 'reshaped', 'ire', 'repositioning', 'gluten', 'meal', 'pickup', 'pellets', 'decatur', 'il', 'cedar', 'rapids', 'ia', 'cereals', 'ecus', 'provisionally', 'optional', 'enjoys', 'subtracting', 'shipyard', 'cherbourg', 'yeard', 'cutback', 'indre', 'managements', 'curtail', 'estates', 'hub', 'gadoon', 'unearthed', 'cages', 'tando', 'allahyar', 'hyderabad', 'recorder', 'sui', 'technologists', 'sugarcane', 'pillaged', 'gujar', 'megawatt', 'mohib', 'textile', 'defaulted', 'leasing', 'modarabas', 'multiply', 'shahid', 'privatisation', 'sindh', 'restraining', 'javedan', 'dadabhoy', 'pvt', 'directions', 'occupying', 'rebelled', 'wrecked', 'mined', 'halo', 'roadsides', 'qotbuddin', 'hilal', 'officiated', 'sprinkling', 'petrol', 'andhra', 'pradesh', 'rarest', 'beings', 'roasted', 'appellants', 'plotted', 'elephant', 'tramples', 'rampaging', 'trampled', 'hari', 'maya', 'poudels', 'madhumalla', 'asleep', 'beast', 'kingdoms', 'elephants', 'overrun', 'kudapokuna', 'welikanda', 'vavuniya', 'undercover', 'vicorp', 'sabourin', 'cfo', 'denver', 'bestop', 'boulder', 'socal', 'edison', 'kilovolt', 'kv', 'wildfire', 'raging', 'conroy', 'crews', 'residues', 'megawatts', 'mw', 'containment', 'arid', 'windy', 'leong', 'munis', 'municipals', 'semiconductor', 'expectation', 'emerge', 'modem', 'networking', 'elias', 'moosa', 'roberston', 'pmc', 'chipsets', 'severance', 'randall', 'soundview', 'focusing', 'develops', 'shrink', 'hambrecht', 'quist', 'slowing', 'housecall', 'sink', 'underperform', 'hospice', 'limitation', 'subsidiaries', 'forma', 'oasis', 'gallagher', 'obscene', 'swore', 'hate', 'supermodel', 'noel', 'laryngitis', 'patsy', 'kensit', 'tickets', 'slough', 'upbeat', 'pretax', 'hammerson', 'warburg', 'stances', 'maintaining', 'rental', 'incomes', 'shaved', 'nigel', 'mobbs', 'birt', 'shrinks', 'seasonally', 'canadians', 'burea', 'shaken', 'backdrop', 'espionage', 'rings', 'jomhuri', 'eslami', 'photographing', 'pan', 'turkism', 'exiles', 'fallahiyan', 'distillates', 'kerosene', 'sort', 'noticeable', 'differential', 'ports', 'exceed', 'cargoes', 'lowest', 'suspicious', 'overhang', 'sulphur', 'manuscript', 'thieves', 'testament', 'akhbar', 'undated', 'pretended', 'wyche', 'invoked', 'recess', 'confirming', 'predecessor', 'mabus', 'shops', 'shutters', 'exporters', 'purchasing', 'destinations', 'southland', 'mainframe', 'delivering', 'constitutes', 'contends', 'nba', 'continually', 'baxter', 'immuno', 'miilion', 'secures', 'cos', 'chipset', 'hotels', 'doubletree', 'toxic', 'compuserve', 'impasse', 'authorised', 'trashed', 'nudge', 'gop', 'reporter', 'beachcomber', 'highlands', 'debris', 'bearing', 'markings', 'conducting', 'fireball', 'wallets', 'shoes', 'mechanical', 'leila', 'corcoran', 'writes', 'enslaved', 'pleads', 'abraham', 'maimed', 'poses', 'volunteers', 'preserved', 'microfilm', 'stowed', 'rarely', 'enlistment', 'discharge', 'certificates', 'unveiling', 'memorial', 'shall', 'oportunity', 'enjoyment', 'sic', 'slavery', 'oppreses', 'liberty', 'outpouring', 'curse', 'separated', 'matron', 'plead', 'dishonourable', 'prisoner', 'thoughtless', 'folly', 'notation', 'regiment', 'pardon', 'anguish', 'amputee', 'terrible', 'haunted', 'posed', 'chested', 'insight', 'rhythms', 'carbondale', 'ulysses', 'writings', 'poetic', 'linguistic', 'treasure', 'trove', 'sociology', 'seared', 'consciousness', 'confederacy', 'retirees', 'budge', 'weidman', 'shepherded', 'predicts', 'databases', 'battlefields', 'discharged', 'incapacity', 'inebetude', 'arise', 'masturbation', 'vow', 'sylvie', 'ajaccio', 'mediterranean', 'defused', 'debre', 'corse', 'lax', 'powerless', 'beauvau', 'nightly', 'figaro', 'shaky', 'racked', 'principally', 'backlash', 'om', 'gruppen', 'omlx', 'pulpex', 'pulp', 'timber', 'recycled', 'complements', 'softs', 'cocoa', 'unpredictable', 'risky', 'hedge', 'tankage', 'naphtha', 'kero', 'throughput', 'bunkering', 'blenkinsop', 'pantomime', 'enacting', 'brandished', 'theatre', 'prop', 'gorleben', 'depot', 'catania', 'sicily', 'slaying', 'nephew', 'puglisi', 'salvatore', 'botta', 'cemetery', 'sicilian', 'hardened', 'tip', 'consciences', 'nitto', 'santapaola', 'knelt', 'ambush', 'cemetary', 'canteen', 'limerick', 'warders', 'takings', 'italians', 'hiv', 'pensioner', 'harassing', 'hookers', 'aids', 'pietro', 'cruising', 'hurling', 'blanks', 'sighted', 'arctic', 'spitzbergen', 'rune', 'ntb', 'vnukovo', 'tupolev', 'longyearbyen', 'airstrip', 'rescuers', 'opera', 'mountainside', 'barentsburg', 'galeforce', 'meteorological', 'cellars', 'trees', 'uprooted', 'roofs', 'trains', 'cm', 'communes', 'rmt', 'turnhout', 'scouts', 'camping', 'meadow', 'hindered', 'excavations', 'jumet', 'lightly', 'tides', 'pesetas', 'chemicals', 'madness', 'overreacted', 'liberte', 'leveilly', 'finns', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'untroubled', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', 'jolts', 'zealands', 'waiau', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 'touching', 'intraday', 'technically', 'doreen', 'siow', 'siam', 'banker', 'bishi', 'int', 'jcr', 'jbri', 'daewoo', 'dacom', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', 'rents', 'haneda', 'itami', 'contesting', 'stymies', 'dismantle', 'considerable', 'directorate', 'bureaucrats', 'mandarin', 'secretaries', 'src', 'distillation', 'stuck', 'varied', 'exchanger', 'petrolem', 'chevron', 'texaco', 'loxley', 'reviewed', 'jobless', 'inem', 'cur', 'prod', 'adj', 'trln', 'fcast', 'def', 'qtr', 'epa', 'annualised', 'ine', 'variation', 'corresponds', 'relate', 'finances', 'obstructing', 'lasa', 'zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', 'schillings', 'fibres', 'economies', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', 'amending', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'stands', 'expertise', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', 'marino', 'hartson', 'samokhalova', 'spirited', 'quigley', 'numerical', 'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', 'tatiana', 'stiajkina', 'arndt', 'vikstedt', 'boyden', 'godras', 'kombouare', 'abderdeen', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', 'bayle', 'norifumi', 'abe', 'luca', 'cadalora', 'criville', 'tadayuki', 'okada', 'checa', 'shinichi', 'itoh', 'swe', 'bradley', 'hughes']\n" + ] + } + ], + "source": [ + "print(itos)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fa981cf6", + "metadata": { + "id": "fa981cf6" + }, + "outputs": [], + "source": [ + "v.set_default_index(v[\"\"])" + ] + }, + { + "cell_type": "markdown", + "id": "4ed6fe85", + "metadata": { + "id": "4ed6fe85" + }, + "source": [ + "### Preprocess data" + ] + }, + { + "cell_type": "code", + "source": [ + "reformat_ner_dict = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}" + ], + "metadata": { + "id": "q3jPiHDvdkEJ" + }, + "id": "q3jPiHDvdkEJ", + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "149c6b1f", + "metadata": { + "id": "149c6b1f" + }, + "outputs": [], + "source": [ + "def fit_data_Y(column):\n", + " dt = [\n", + " [reformat_ner_dict[token] for token in row.split()] for row in column\n", + " ]\n", + " return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]\n", + "\n", + "def fit_data_X(dt):\n", + " return [\n", + " torch.tensor(\n", + " [v[\"\"]] + [v[token] for token in document.split()] + [v[\"\"]],\n", + " dtype=torch.long,\n", + " )\n", + " for document in dt\n", + " ]\n", + "\n", + "\n", + "train_X = fit_data_X(train_data['text'])\n", + "train_Y = fit_data_Y(train_data['label'])\n", + "dev_X = fit_data_X(dev_texts_data['text'])\n", + "dev_Y = fit_data_Y(dev_labels_data['label'])\n", + "test_X = fit_data_X(test_texts_data['text'])" + ] + }, + { + "cell_type": "markdown", + "id": "1fa44315", + "metadata": { + "id": "1fa44315" + }, + "source": [ + "### Create model" + ] + }, + { + "cell_type": "code", + "source": [ + "num_tags = len(reformat_ner_dict)" + ], + "metadata": { + "id": "jAUWEqOse936" + }, + "id": "jAUWEqOse936", + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1eeecf36", + "metadata": { + "id": "1eeecf36" + }, + "outputs": [], + "source": [ + "class LSTM(torch.nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(LSTM, self).__init__()\n", + " self.emb = torch.nn.Embedding(len(v.get_itos()), 100)\n", + " self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n", + " self.fc1 = torch.nn.Linear(256, num_tags)\n", + "\n", + " def forward(self, x):\n", + " emb = torch.relu(self.emb(x))\n", + " lstm_output, (h_n, c_n) = self.rec(emb)\n", + " out_weights = self.fc1(lstm_output)\n", + " return out_weights" + ] + }, + { + "cell_type": "code", + "source": [ + "lstm = LSTM()\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(lstm.parameters())" + ], + "metadata": { + "id": "CIRuZkFjfaBc" + }, + "id": "CIRuZkFjfaBc", + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Evaluation model" + ], + "metadata": { + "id": "wJ4trgNPfcPF" + }, + "id": "wJ4trgNPfcPF" + }, + { + "cell_type": "code", + "source": [ + "def get_accuracy(y_true, y_pred):\n", + " hit = 0\n", + " missed = 0\n", + "\n", + " for p, t in zip(y_pred, y_true):\n", + " if p == t:\n", + " hit += 1\n", + " else:\n", + " missed += 1\n", + "\n", + " accuracy = hit / (hit + missed)\n", + " return accuracy" + ], + "metadata": { + "id": "8zkCdkWTf472" + }, + "id": "8zkCdkWTf472", + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def eval_model(dataset_tokens, dataset_labels, model):\n", + " Y_true = []\n", + " Y_pred = []\n", + " for i in tqdm(range(len(dataset_labels))):\n", + " batch_tokens = dataset_tokens[i].unsqueeze(0)\n", + " tags = list(dataset_labels[i].numpy())\n", + " Y_true += tags\n", + "\n", + " Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n", + " Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n", + " Y_pred += list(Y_batch_pred.numpy())\n", + "\n", + " return get_accuracy(Y_true, Y_pred)" + ], + "metadata": { + "id": "ljxy_LC-feYD" + }, + "id": "ljxy_LC-feYD", + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "ec76b0f6", + "metadata": { + "id": "ec76b0f6" + }, + "source": [ + "### Train model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e72a055c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 839, + "referenced_widgets": [ + "148e531ca4d246bab191b479727a590f", + "457151c3036747b2a00fda8f10de0286", + "c6ff63b96e3242ab85ab11b705e9589d", + "244d3485460448fca41d62275736ff13", + "e763556731d44e848d1ebdaa135a2f4f", + "4861711062e848e1a8fe201dd10b8d9b", + "3892a6398cc04afba3286f09fc9121f7", + "206dc08bbad24bbb9fd3a882d7364b9b", + "9d9270f1f9f54d00904557d9dd69551c", + "d927c26539ce4962939e455a4e64f9a0", + "97eaa10132f0495d9c27a2351fb66e1a", + "8c158138b4074904a47ca2c4ae43e0bc", + "95ea84f571a74e9aa61667dfb445221e", + "f017002d6ff6489e95b622b4338ec770", + "03a0450df07f4ceeafc67a300e5268d6", + "9ea9df37e643499282419a51f54dcc14", + "0b35eabf7fb54d989ea02464a068afef", + "1ae3188960b3468caffafd939ad1e34e", + "b0371c1241af4fc5a79eb7b76fecbd26", + "de23364b23324aa3a707d7a1c8a433a1", + "bb9e8c1382314edabd9060436197f3d8", + "dc677bc444ba4712b3b5699dd399b72b", + 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(dev_accuracy_history, label='Validation Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Model Accuracy Over Epochs')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4d0b9315", + "metadata": { + "id": "4d0b9315" + }, + "source": [ + "### Predict and save results" + ] + }, + { + "cell_type": "code", + "source": [ + "reversed_ner_dict = {v: k for k, v in reformat_ner_dict.items()}\n", + "print(reversed_ner_dict)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T9rzUXtEpEbd", + "outputId": "dca59368-894e-47f2-c47f-a57dd99ff1c0" + }, + "id": "T9rzUXtEpEbd", + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "54f93a9b", + "metadata": { + "id": "54f93a9b" + }, + "outputs": [], + "source": [ + "def predict_and_save(X, filename):\n", + " Y_predicted = []\n", + " for i in tqdm(range(len(X))):\n", + " batch_tokens = X[i].unsqueeze(0)\n", + " Y_batch_pred_weights = lstm(batch_tokens).squeeze(0)\n", + " Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n", + " Y_processed_pred = \" \".join(reversed_ner_dict[item] for item in Y_batch_pred.numpy()[1:-1])\n", + " Y_predicted.append(Y_processed_pred)\n", + " Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])\n", + " Y_predicted_df.to_csv(filename, sep='\\t', index=False, header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9d3b3867", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "b7a8863307a64d8f8b203cdb96760077", + "acb7d3bfb2ae43f1954624d99eb10eec", + "b6ff9cf63a18425ca23d15b29668a463", + "ecb30112228140788f41d5b6063dc614", + "1d94ae2bd9fd4f08a0a52f58e75653a8", + "ae5ffdb4e84f40978b60679b533f5fd9", + "e0fda431ac3043dfb47bb07732d674e4", + "e48dba51dc3c4e149250c7c047a11508", + "eba9b1f144b443a9a684906c2b90cfd2", + "e9145b0563704a41be8268b4672ddebc", + "182700175c48491794c1376c5715295f", + "7169a263c7a2461fa9a2f997ce90005f", + "32c32e707d5b430aa42c1225ee7b79fb", + "6b94b3b3c1dd4898b0b86192a723d60f", + "fbd896be2b66413ea63dcf73ab66bd3c", + "865e52e7f2fd40d7b5707bee5f348291", + "ce50ad2366d4453fa2cfc80cd3d91435", + "216ad5eb5e2741aab4cd37315e9110bd", + "cb8040b141d74672ad2d54ff3e2ba9fa", + "c0a90377ae85497fbf335ffa7f5466d5", + "34b793f8dbf849359774dced56ce29a5", + "9c1a9bf9196b4233a7476a440222798d" + ] + }, + "id": "9d3b3867", + "outputId": "1d6eda7b-888a-43a2-c0d8-698c2987ff35" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/215 [00:00